Project Abstract

The overall goal of the CLASSiC project is to
facilitate the rapid deployment of accurate and robust spoken dialogue
systems that can learn from experience. The approach is based on
statistical learning methods with a unified treatment of uncertainty
across the entire system (speech recognition, spoken language
understanding, dialogue management, natural language generation,
and speech synthesis). This will result in a modular processing framework
with an explicit representation of uncertainty connecting the various
sources of uncertainty (understanding errors, ambiguity, etc) to the
constraints to be exploited (task, dialogue, and user contexts). The
architecture supports a layered hierarchy of supervised learning and
reinforcement learning in order to facilitate mathematically
principled optimisation and adaptation techniques. It is being developed in close cooperation with our industrial partner in order to
ensure a practical deployment platform as well as a flexible research
test-bed.

Partners

Expected
final results

The CLASSIC project
aims at a qualitative leap in the robustness, flexibility, efficiency and
naturalness of spoken dialogue systems, through new technologies
based on the paradigm of computational learning and statistical
modelling.

The
project is expected to produce new mathematical models and
computational techniques for spoken language understanding, dialogue
management, and natural language generation. It will produce 4
different “showcase” spoken dialogue systems illustrating these
advances, and it will provide new software and corpora for the future
development of statistical spoken dialogue systems. Computational
learning techniques will also be integrated with industry standard
dialogue system development tools used by our industry partner,
France Telecom/Orange Labs.

Potential
Impact and Use

For end users
(i.e. members of the general public and professionals who will use
spoken dialogue systems in the future) the impact of CLASSiC will
ultimately be more useable, robust, and efficient human-computer
spoken dialogue interfaces, which are context-aware and adaptive.

CLASSiC
therefore develops key technology and tools which will help meet
some of the general goals of the ICT programme in FP7 – tools for
the delivery of information technology to different individuals in a
natural, user-tailored, adaptive, and intuitive manner. Speech
interfaces are inherently inclusive in their support for non-expert
(and even illiterate) users, who are only required to have basic
spoken conversational skills in order to interact with IT services
and devices.

One potential
impact of CLASSIC is the development of a new paradigm for generic
technology which will enable human-computer interaction in a
conversational, user- adaptive manner, based on the user and their
situation. This type of technology contributes to the EU’s
objectives of providing European citizens with more efficient, robust
access to IT services, and removing educational and linguistic
barriers.